A intelligent particle swarm optimization for short-term traffic flow forecasting using on-road sensor systems
On-road sensor systems installed on freeways are used to capture traffic flow data for short-term traffic flow predictors for traffic management, in order to reduce traffic congestion and improve vehicular mobility. This paper intends to tackle the impractical time-invariant assumptions which underl...
| Main Authors: | , , |
|---|---|
| Format: | Journal Article |
| Published: |
Institute of Electrical and Electronic Engineers
2013
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/41959 |
| _version_ | 1848756287491276800 |
|---|---|
| author | Chan, Kit Yan Dillon, Tharam Chang, Elizabeth |
| author_facet | Chan, Kit Yan Dillon, Tharam Chang, Elizabeth |
| author_sort | Chan, Kit Yan |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | On-road sensor systems installed on freeways are used to capture traffic flow data for short-term traffic flow predictors for traffic management, in order to reduce traffic congestion and improve vehicular mobility. This paper intends to tackle the impractical time-invariant assumptions which underlie the methods currently used to develop short-term traffic flow predictors: i) the characteristics of current data captured by on-road sensors are assumed to be time-invariant with respect to those of the historical data, which is used to developed short-term traffic flow predictors; and ii) the configuration of the on-road sensor systems is assumed to be time-invariant. In fact, both assumptions are impractical in the real world, as the current traffic flow characteristics can be very different from the historical ones, and also the on-road sensor systems are time-varying in nature due to damaged sensors or component wear. Therefore, misleading forecasting results are likely to be produced when short-term traffic flow predictors are designed using these two time-invariant assumptions. To tackle these time-invariant assumptions, an intelligent particle swarm optimization algorithm, namely IPSO, is proposed to develop short-term traffic flow predictors by integrating the mechanisms of particle swarm optimization, neural network and fuzzy inference system, in order to adapt to the time-varying traffic flow characteristics and the time-varying configurations of the on-road sensor systems. The proposed IPSO was applied to forecast traffic flow conditions on a section of freeway in Western Australia, whose traffic flow information can be captured on-line by the on-road sensor system. These results clearly demonstrate the effectiveness of using the proposed IPSO for real-time traffic flow forecasting based on traffic flow data captured by on-road sensor systems. |
| first_indexed | 2025-11-14T09:09:48Z |
| format | Journal Article |
| id | curtin-20.500.11937-41959 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:09:48Z |
| publishDate | 2013 |
| publisher | Institute of Electrical and Electronic Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-419592017-09-13T16:03:54Z A intelligent particle swarm optimization for short-term traffic flow forecasting using on-road sensor systems Chan, Kit Yan Dillon, Tharam Chang, Elizabeth particle swarm optimization sensor systems traffic contingency fuzzy inference system traffic flow forecasting neural networks time-varying systems sensor data On-road sensor systems installed on freeways are used to capture traffic flow data for short-term traffic flow predictors for traffic management, in order to reduce traffic congestion and improve vehicular mobility. This paper intends to tackle the impractical time-invariant assumptions which underlie the methods currently used to develop short-term traffic flow predictors: i) the characteristics of current data captured by on-road sensors are assumed to be time-invariant with respect to those of the historical data, which is used to developed short-term traffic flow predictors; and ii) the configuration of the on-road sensor systems is assumed to be time-invariant. In fact, both assumptions are impractical in the real world, as the current traffic flow characteristics can be very different from the historical ones, and also the on-road sensor systems are time-varying in nature due to damaged sensors or component wear. Therefore, misleading forecasting results are likely to be produced when short-term traffic flow predictors are designed using these two time-invariant assumptions. To tackle these time-invariant assumptions, an intelligent particle swarm optimization algorithm, namely IPSO, is proposed to develop short-term traffic flow predictors by integrating the mechanisms of particle swarm optimization, neural network and fuzzy inference system, in order to adapt to the time-varying traffic flow characteristics and the time-varying configurations of the on-road sensor systems. The proposed IPSO was applied to forecast traffic flow conditions on a section of freeway in Western Australia, whose traffic flow information can be captured on-line by the on-road sensor system. These results clearly demonstrate the effectiveness of using the proposed IPSO for real-time traffic flow forecasting based on traffic flow data captured by on-road sensor systems. 2013 Journal Article http://hdl.handle.net/20.500.11937/41959 10.1109/TIE.2012.2213556 Institute of Electrical and Electronic Engineers fulltext |
| spellingShingle | particle swarm optimization sensor systems traffic contingency fuzzy inference system traffic flow forecasting neural networks time-varying systems sensor data Chan, Kit Yan Dillon, Tharam Chang, Elizabeth A intelligent particle swarm optimization for short-term traffic flow forecasting using on-road sensor systems |
| title | A intelligent particle swarm optimization for short-term traffic flow forecasting using on-road sensor systems |
| title_full | A intelligent particle swarm optimization for short-term traffic flow forecasting using on-road sensor systems |
| title_fullStr | A intelligent particle swarm optimization for short-term traffic flow forecasting using on-road sensor systems |
| title_full_unstemmed | A intelligent particle swarm optimization for short-term traffic flow forecasting using on-road sensor systems |
| title_short | A intelligent particle swarm optimization for short-term traffic flow forecasting using on-road sensor systems |
| title_sort | intelligent particle swarm optimization for short-term traffic flow forecasting using on-road sensor systems |
| topic | particle swarm optimization sensor systems traffic contingency fuzzy inference system traffic flow forecasting neural networks time-varying systems sensor data |
| url | http://hdl.handle.net/20.500.11937/41959 |